Every time a large model undergoes a capability iteration, there is a wave of "industrial software termination theory" in the industry. On one side, AI entrepreneurs are shouting 'Refactoring industrial software with big models', as if a set of big models can penetrate the moat that Dassault, Siemens, and ANSYS have accumulated for half a century; On the other hand, traditional industrial software manufacturers are busy adding AI chat boxes to their products, packaging "AI assisted drawing" and "AI automatic grid drawing" as "AI native", insisting that AI is just a plugin to improve efficiency and cannot touch the foundation of industrial software.
Behind the two extreme voices lies the same cognitive misconception: everyone is talking about "what AI can bring to industrial software", but no one returns to the fundamental question - why was industrial software born? Why do we really need it?
Without understanding this' meta problem ', all discussions about' subversion 'and' moat 'are groundless. To answer whether industrial software will be killed by AI, we must start from the essence of industrial software and gradually deduce the ultimate outcome.
1The Birth of Industrial Software: Why Do We Need Industrial Software?
The inherent nature of industrial software has never been "software", but a convergence tool for industrial system uncertainty.The ultimate demand of industrial production has always been one:Using the lowest cost, shortest cycle, highest quality, and minimum risk, complete the full process transformation of industrial products from "human demand" to "physical implementation", transforming the non-standard, random, error prone, and uncontrollable physical world into standardized, deterministic, reusable, and predictable stable output, and achieving socialized mass production。
The biggest enemy of the industrial system has always been "uncertainty": human error in hand drawn drawings, high cost of physical trial and error, production fluctuations caused by manual control, information gaps in cross job collaboration, and the irreplicability of master experience... These uncertainties are the "entropy increase" of the industrial system and the core bottleneck that restricts the development of industrial scale.
The birth of industrial software is to solve this core problem. Its core mission is to continuously transform the implicit experience, non-standard judgments, and manual operations that can only be mastered by humans in the industrial field into digital rules that can be encoded, solidified, reused, and automatically executed, and to continuously converge the entropy increase of the industrial system.
🔷 The replacement of hand drawn drawings with CAD has converged the errors and non standardization of manual drawing, providing a unified and reusable digital standard for product design;
🔷 CAE simulation replaces physical trial and error, which converges the uncertainty and trial and error costs of product development, allowing physical experiments that originally required several months and tens of millions to be verified in a few days in the digital world;
🔷 MES/DCS replaces manual control of production lines, which converges the fluctuations and uncontrollability of the production process, providing a stable control tool for the quality, efficiency, and cost of large-scale production;
🔷 PLM replaces manual management of documents and BOMs, which converges data chaos and collaborative losses throughout the entire lifecycle, providing a unified digital carrier for cross departmental and cross enterprise collaboration.
This is the essence of industrial software: its core value has never been the code itself, but the industrial mechanisms, process knowledge, industry experience, and process specifications that are embedded in the code. It is the digital carrier of industrial know-how and the underlying tool that sets the rules for the industrial world.
2、 What problems have industrial software solved in the past few decades?
By understanding the essence, we can clearly break down the problems solved by industrial software in the past into two layers:
🔷 The first layer is the problem of the industry itself - converging uncertainty, solidifying industrial rules, reducing trial and error costs, and achieving large-scale production. This is the core of industrial software and the fundamental meaning of its birth.
🔷 The second layer is the problem brought by "people" - because in the past industrial system, people were the only decision-making and execution subject in the entire process, and industrial software had to adapt to people's ability boundaries, division of labor modes, and collaborative logic. This is the form shell of industrial software, an inevitable product of a specific era.
The vast majority of industrial software forms we see today, from complex UI interfaces, fragmented functional modules, to cumbersome approval processes and version management systems, 90% are not designed to solve industrial problems themselves, but to adapt to the core node of "human".
Why do CAD, CAE, CAM, and PLM need to be separated into independent software modules? It's not that industrial processes must be broken down, but rather that people's professions, occupations, and positions are broken down. Designers use CAD, simulation engineers use CAE, process engineers use CAM, project managers use PLM, and software must match the division of labor of people in order to be used.
Why do industrial software require complex operations with hundreds of menus and years of learning to master? It's not that industrial problems must be so complex, but rather that people need to operate, understand, and verify step by step. Software must provide a "ladder" for people to operate, so that they can transform their professional abilities into industrial output through software.
Why does PLM have a complex system for change approval, version control, and division of responsibilities? It is not necessary for industrial processes to have these links, but to solve the "trust, responsibility, and interest disputes between people" - to prevent problems, disputes, information asymmetry, and unclear rights and responsibilities, essentially dealing with the "human world".
Why is it almost impossible for industrial software from different industries to be universally used? The barriers between PLM in the automotive industry and PLM in the aviation industry are distinct, not because there is a fundamental difference in industrial mechanisms, but because people in different industries have different process experience, process specifications, and work habits, and software must adapt to the industry attributes of people.
In summary, over the past few decades, industrial software has been doing two things - one is to "set rules" for the industrial world, and the other is to "build a ladder" for the "people" who operate the rules. The vast majority of industrial software we are debating and seeing today is the 'ladder' rather than the 'rules' themselves.
3、 What have the core issues in the industrial field changed in the era of AI?
AI， Especially the breakthrough in capabilities brought about by large models has precisely broken through the core premise of the industrial system in the past few decades: people are no longer the necessary executing and decision-making subjects for the entire industrial process.
For the first time, the universal big model has enabled digital systems to handle implicit knowledge and complex coupling problems that humans cannot break down, logicalize, or encode. Previously, requirements decomposition, design iteration, simulation verification, process planning, and collaborative docking that had to be completed by humans can now be independently completed by AI; In the past, industrial software had to adapt to human capability boundaries, division of labor patterns, and collaborative logic, but now with people leaving the execution layer, it has completely lost its meaning of existence.
This leads to the sharpest soul questioning: when the analysis, design, testing, delivery, and manufacturing of industrial activities are all carried out by AI, and there are no more people left, whose efficiency can industrial software improve? If it's just about optimizing computing power, algorithms, and models, is it still related to industrial software itself?
To answer this question, we must first recognize that in the era of AI, the ultimate goal of the industrial sector has not changed - it is still to converge uncertainty and achieve low-cost, high-quality, and large-scale industrial production. But the core bottleneck of the industrial system has completely changed.
In the past, the core bottleneck of industrial systems was the boundary of human abilities: long learning cycles, operational errors, limited energy, and loss of collaboration. The core goal of industrial software is to help people improve efficiency and break through human ability boundaries.
而AI时代，工业体系的核心瓶颈，变成了AI的工业落地可靠性：通用AI的核心逻辑是“概率生成”，允许“大概对”，甚至允许幻觉；但工业领域的核心逻辑是“绝对确定性”，一个参数的偏差，就可能导致产品报废、产线停摆、甚至重大安全事故，零容错是不可突破的底线。
通用AI能生成天马行空的设计，但它无法自主判断：
👉 这个设计是否符合行业法规？
👉 材料疲劳强度能否满足10年使用寿命？
👉 生产方案能否适配现有产线的设备精度？
👉 极端工况下会不会出现安全风险？
通用AI能完成单点的推理任务，但它无法自主完成：从需求拆解、设计迭代、仿真验证、工艺规划、生产排程、供应链协同、运维保障的全流程闭环，无法协调多环节、多主体、多物理场的耦合与协同。
这就是AI和工业之间，一道天然的、无法靠通用算力、算法、大模型跨越的鸿沟。AI解决了“人”这个过去的核心瓶颈，但也带来了新的核心问题：怎么让AI的通用智能，转化为可落地、可校验、零容错、可闭环的工业生产力？
而这个问题，恰恰是工业软件的核心使命，也是它在AI时代不可替代的根本原因。
四、AI时代，工业软件的定义该被重新书写了
行业里对工业软件的固化定义，本质上是过去工业问题的解决方案合集，是基于“人是工业全流程核心决策节点”这个前提，划分出的研发设计、生产制造、经营管理、运维服务四大类。
但我们必须承认：技术变了，工业领域的核心问题变了，工业软件的定义和边界，就绝对不能固化。
基于我们对工业软件本质的推演，AI时代的工业软件，应该有全新的定义：
AI时代的工业软件，是工业领域全生命周期知识、机理、规则、流程的数字化封装与可执行能力集合，是工业AI智能体的核心本体；它赋予AI在工业场景下可落地、可校验、可闭环、可进化的需求拆解、设计研发、生产制造、交付运维全流程问题解决能力，是数字时代工业体系自主进化的核心数字基础设施。
这个定义，彻底推翻了过去对工业软件的固化认知，也清晰地回应了那个灵魂拷问：
🔷 第一，工业软件的服务对象，从“人”彻底转向了“工业AI Agent”。它不再需要给人搭操作的梯子，不再需要适配人的分工、习惯、能力边界，只需要给AI提供可调用、可执行、带护栏、可追溯的工业能力。
🔷 第二，工业软件的核心价值，从“提升人的效率”，变成了“定义AI的工业能力边界”。所谓“没人了就不用提效”，本质上是混淆了“手段”和“目的”——提升人的效率，从来不是工业软件的终极目的，只是特定时代的手段。它的终极目的，是提升整个工业体系从“需求”到“物理落地”的转化效率，收敛整个工业体系的不确定性。这个使命，在AI时代不仅没有消失，反而变得更加重要。
🔷 第三，工业软件的核心本体，从“套装工具软件”，变成了“可执行的工业能力集合”。它不再是一行行写死的代码、一个个拆分的功能模块，而是把上百年沉淀的工业Know-how、机理模型、合规规则，封装成AI可以直接调用的能力单元。通用AI的算力、算法，只是工业AI的“身体”，而工业软件的能力集合，才是工业AI的“灵魂”。
一句话说透：AI杀不死工业软件，只会杀死工业软件里，那些为了适配人而存在的冗余形态。真正的工业软件内核，会从“给人用的工具”，变成“AI的工业灵魂”。
五、未来的工业软件，到底会以什么形态存在？
既然工业软件的核心本体，是面向AI Agent的工业能力集合，那它的终局形态，就绝不是今天我们看到的套装软件，也不是行业里热议的RAG、MCP这些技术组件—这些只是搭建终局形态的“钢筋水泥”，而非“工业大厦”本身。
🔷 RAG（检索增强生成），只是未来工业软件的知识检索与校验组件，它解决的是AI的知识调用、幻觉抑制问题，但无法执行工业动作、校验工业机理、闭环工业流程，永远成不了工业软件的本体。
🔷 MCP（模型上下文协议），只是未来工业软件的通信协同协议，它解决的是AI与能力模块、多AI之间的上下文同步问题，本身不承载任何工业能力，没有工业内核的MCP，只是一套空的协议框架。
🔷 工业AI Agent，只是未来工业软件的执行主体，是工业软件的“使用者”，而非工业软件本身。就像人是传统工业软件的使用者，而非软件本身，Agent能发挥多大的工业能力，完全取决于底层工业软件的能力沉淀。
未来工业软件的终局形态，是面向工业AI Agent的工业原生智能体操作系统（Industrial Agent OS，简称IAOS），传统工业软件的所有核心价值，最终都会被拆解、重构、封装为这个操作系统的核心内核。
这个操作系统，完全剥离了所有为适配人而存在的冗余设计，由5层核心架构构成，每一层都严格锚定工业软件的本质使命：
1.原子化工业能力内核（未来工业软件的核心本体）
这是整个操作系统的灵魂，也是传统工业软件的最终归宿。它把传统CAD/CAE/CAM/EDA/MES/PLM等所有工业软件的核心价值，彻底拆解、重构、封装为原子化、可组合、可执行、带护栏、可追溯的工业能力算子。
不再是一整个CAD套装，而是拆解为三维参数化建模算子、BOM自动生成算子、GD&T合规校验算子；不再是一整个CAE软件，而是拆解为结构力学仿真算子、流体场仿真算子、疲劳寿命校验算子。每个算子都自带工业机理护栏、合规边界、误差校验逻辑，AI调用时，会自动完成执行、校验、纠错，从根源上杜绝工业幻觉。
未来工业软件厂商的核心竞争，从来不是AI算法，而是这个算子库的深度、广度与可靠性-这是通用AI永远无法替代的、沉淀了上百年的工业护城河。
2.工业级知识与数据底座
这是操作系统的“知识弹药库”，是工业能力算子的配套支撑，也是工业增强版RAG的核心载体。它构建了工业多模态知识图谱+全生命周期时序数据湖，包含全球工业机理公式、行业标准、法规规范、工艺案例、故障数据库等全量工业知识，实现“检索-校验-关联-更新-闭环”的全链路能力，为AI决策提供全量、准确、动态更新的工业知识支撑。
3.工业Agent通信与协同协议层
这是操作系统的“神经网络”，也是工业增强版MCP的核心载体。它解决了三大工业协同痛点：单Agent内部不同能力算子的协同调用、同企业内多工业Agent的跨场景协同、产业链上下游跨主体Agent的安全协同。同时新增了权责划分、知识产权隔离、合规审计等工业专属能力，完全适配工业场景的强合规、强安全需求。
4.工业Agent调度与编排引擎
这是操作系统的“大脑中枢”，是连接通用AI能力与工业能力内核的核心桥梁。它基于人类输入的顶层工业目标，自主完成目标拆解、算子调度、流程编排、多Agent协同、风险预判、闭环优化，真正实现从顶层需求到物理落地的端到端自主执行。
5.极简人机交互入口层
这是整个系统唯一和人相关的部分，彻底剥离了所有冗余设计，仅保留两个核心入口：顶层目标自然语言输入口、最终结果与极端异常告警输出口。人类只需要输入核心工业目标与约束条件，不需要懂任何软件操作、工业细节，中间所有执行过程完全黑盒化，彻底回归了工业软件解决工业问题的本质。
六、终局答案：工业软件不会被AI绝杀，只会被AI唤醒
写到这里，标题的问题已经有了清晰的答案：工业软件不会被AI绝杀，恰恰相反，AI会让工业软件，彻底摆脱“工具”的枷锁，回归它诞生时的本源使命。
过去，工业软件的能力边界，被人的认知边界、操作能力、协同效率锁死了，它不得不花大量的精力，给人搭梯子、做适配，反而偏离了“给工业世界定规则、收敛不确定性”的核心使命。
未来，AI会彻底拆掉那把用了几十年的“梯子”，让工业软件的核心内核，直接成为工业AI的灵魂。它不再需要适配人的能力，只需要专注于工业本身的规则、机理、知识与规律，它的能力边界，会从“人类已知的工业知识”，拓展到“AI可发现的无限工业规律”。
从来不是AI绝杀工业软件，而是工业软件，会带着AI，真正走进工业的深水区。